RT Journal Article
T1 Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand
A1 Casteleiro Roca, José Luis
A1 Barragán Piña, Antonio Javier
A1 Segura Manzano, Francisca
A1 Calvo Rolle, José Luis
A1 Andújar Márquez, José Manuel
AB Hydrogen-based energy storage and generation is an increasingly used technology,especially in renewable systems because they are non-polluting devices. Fuel cells are complexnonlinear systems, so a good model is required to establish efficient control strategies. This paperpresents a hybrid model to predict the variation of H2 flow of a hydrogen fuel cell. This modelcombining clusters’ techniques to get multiple Artificial Neural Networks models whose resultsare merged by Polynomial Regression algorithms to obtain a more accurate estimate. The modelproposed in this article use the power generated by the fuel cell, the hydrogen inlet flow, and thedesired power variation, to predict the necessary variation of the hydrogen flow that allows thestack to reach the desired working point. The proposed algorithm has been tested on a real protonexchange membrane fuel cell, and the results show a great precision of the model, so that it can bevery useful to improve the efficiency of the fuel cell system.
PB MDPI
SN 2079-9292
YR 2019
FD 2019-11
LK http://hdl.handle.net/10272/17576
UL http://hdl.handle.net/10272/17576
LA spa
NO Casteleiro Roca, J. L., Barragán Piña, A. J., Segura Manzano, F., Calvo Rolle, J. L.,  Andújar Márquez, J. M. (2019). Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand. Electronics, 8(11), 1325. DOI: https://doi.org/10.3390/electronics8111325
NO This work has been funded by the Spanish Ministry of Economy Industry and Competitiveness throughthe H2SMART-mGRID (DPI2017-85540-R) project
DS Repositorio Institucional de la Universidad de Huelva
RD 30 may 2026